In current motion learning systems, a student acquires motor skills by viewing and imitating the motions of a teacher. The motion sequences of the student and teacher are typically recorded by video cameras and stored on video tape. Some systems digitize the video image and store the motion sequence in computer memory. The student views the video or computer animation and compares his motion to the motion of the teacher.
Systems such as SELSPOT and Optotrack are popular human motion analysis systems. A plurality of light emitting diode markers are fixed to anatomically-interesting locations on a subject. A computer activates the markers in a predetermined order as video cameras record the motion of the subject. Signal processing software compiles a motion sequence of the subject which is viewed on a monitor as an animated humanoid or as a stick figure.
Another popular system uses reflective markers (Elite) instead of light emitting diodes. A light source mounted on the video camera periodically illuminates the markers. Software processes the video image, creating a motion sequence of the subject.
In each of the above systems, a plurality of cameras are required for three dimensional motion analysis. Systems with more than one camera require complex synchronization hardware and more robust signal processing software. An additional drawback with these systems is that a marker must be visible simultaneously by two cameras to determine three dimensional positioning of the marker. As the subject moves, most markers are hidden in at least a few frames, reducing system accuracy.